Overview

Dataset statistics

Number of variables29
Number of observations406431
Missing cells0
Missing cells (%)0.0%
Duplicate rows413
Duplicate rows (%)0.1%
Total size in memory89.9 MiB
Average record size in memory232.0 B

Variable types

Categorical21
Numeric8

Alerts

Dataset has 413 (0.1%) duplicate rowsDuplicates
COMISARIA has a high cardinality: 1041 distinct values High cardinality
DIRECCION has a high cardinality: 1627 distinct values High cardinality
DIST_CIA has a high cardinality: 791 distinct values High cardinality
DIST_HECHO has a high cardinality: 1331 distinct values High cardinality
PROV_CIA has a high cardinality: 187 distinct values High cardinality
PROV_HECHO has a high cardinality: 190 distinct values High cardinality
REGION has a high cardinality: 54 distinct values High cardinality
UBICACION has a high cardinality: 379092 distinct values High cardinality
PAIS_NATAL has a high cardinality: 153 distinct values High cardinality
FEC_REGISTRO_ANIO is highly correlated with FECHA_HORA_HECHO_ANIOHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FEC_REGISTRO_ANIO is highly correlated with FECHA_HORA_HECHO_ANIOHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FEC_REGISTRO_ANIO is highly correlated with FECHA_HORA_HECHO_ANIOHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPOHigh correlation
REGION is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
DPTO_CIA is highly correlated with REGION and 1 other fieldsHigh correlation
SUB_TIPO is highly correlated with MODALIDADHigh correlation
TIPO is highly correlated with FEC_REGISTRO_ANIO and 1 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
DPTO_HECHO is highly correlated with REGION and 1 other fieldsHigh correlation
MODALIDAD is highly correlated with SUB_TIPOHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
LIBRO is highly correlated with REGION and 1 other fieldsHigh correlation
MODALIDAD is highly correlated with SUB_TIPOHigh correlation
REGION is highly correlated with DPTO_CIA and 6 other fieldsHigh correlation
SUB_TIPO is highly correlated with MODALIDADHigh correlation
TIPO is highly correlated with REGION and 3 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
VIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
FEC_REGISTRO_ANIO is highly correlated with REGION and 2 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FEC_REGISTRO_DIA_SEM is highly correlated with FECHA_HORA_HECHO_DIA_SEMHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FECHA_HORA_HECHO_DIA_SEM is highly correlated with FEC_REGISTRO_DIA_SEMHigh correlation
FEC_REGISTRO_DIA_SEM has 70685 (17.4%) zeros Zeros
FECHA_HORA_HECHO_DIA_SEM has 62021 (15.3%) zeros Zeros

Reproduction

Analysis started2022-08-07 23:48:57.149642
Analysis finished2022-08-07 23:49:51.397098
Duration54.25 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

COMISARIA
Categorical

HIGH CARDINALITY

Distinct1041
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
COMISARIA DE LA FAMILIA
 
26982
DE LA FAMILIA
 
6268
SOL DE ORO
 
5375
VITARTE
 
4666
BELLAVISTA
 
4091
Other values (1036)
359049 

Length

Max length46
Median length33
Mean length12.87583132
Min length3

Characters and Unicode

Total characters5233137
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)< 0.1%

Sample

1st rowHUANCHACO
2nd rowDULANTO
3rd rowJICAMARCA
4th rowOCOÑA
5th rowPARCONA

Common Values

ValueCountFrequency (%)
COMISARIA DE LA FAMILIA26982
 
6.6%
DE LA FAMILIA6268
 
1.5%
SOL DE ORO5375
 
1.3%
VITARTE4666
 
1.1%
BELLAVISTA4091
 
1.0%
SANTA ANITA3900
 
1.0%
COMISARIA DE MUJERES DE VILLA EL SALVADOR3893
 
1.0%
EL TAMBO3141
 
0.8%
HUAYCAN2997
 
0.7%
LA HUAYRONA2914
 
0.7%
Other values (1031)342204
84.2%

Length

2022-08-07T18:49:51.495103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de96672
 
11.2%
la64952
 
7.5%
comisaria43730
 
5.1%
familia43471
 
5.0%
san25003
 
2.9%
santa16641
 
1.9%
el16626
 
1.9%
mujeres15676
 
1.8%
10224
 
1.2%
villa9926
 
1.1%
Other values (1084)522727
60.4%

Most occurring characters

ValueCountFrequency (%)
A940260
18.0%
459844
 
8.8%
I399434
 
7.6%
L365169
 
7.0%
E364565
 
7.0%
R306341
 
5.9%
O281371
 
5.4%
C266208
 
5.1%
S246214
 
4.7%
N235783
 
4.5%
Other values (37)1367948
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4731382
90.4%
Space Separator459844
 
8.8%
Dash Punctuation23818
 
0.5%
Other Punctuation8815
 
0.2%
Decimal Number8580
 
0.2%
Other Letter320
 
< 0.1%
Open Punctuation189
 
< 0.1%
Close Punctuation189
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A940260
19.9%
I399434
 
8.4%
L365169
 
7.7%
E364565
 
7.7%
R306341
 
6.5%
O281371
 
5.9%
C266208
 
5.6%
S246214
 
5.2%
N235783
 
5.0%
M225358
 
4.8%
Other values (22)1100679
23.3%
Decimal Number
ValueCountFrequency (%)
12838
33.1%
01966
22.9%
21676
19.5%
9976
 
11.4%
6780
 
9.1%
3320
 
3.7%
524
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
-16932
71.1%
6886
28.9%
Other Punctuation
ValueCountFrequency (%)
.8814
> 99.9%
/1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
459844
100.0%
Other Letter
ValueCountFrequency (%)
º320
100.0%
Open Punctuation
ValueCountFrequency (%)
(189
100.0%
Close Punctuation
ValueCountFrequency (%)
)189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4731702
90.4%
Common501435
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A940260
19.9%
I399434
 
8.4%
L365169
 
7.7%
E364565
 
7.7%
R306341
 
6.5%
O281371
 
5.9%
C266208
 
5.6%
S246214
 
5.2%
N235783
 
5.0%
M225358
 
4.8%
Other values (23)1100999
23.3%
Common
ValueCountFrequency (%)
459844
91.7%
-16932
 
3.4%
.8814
 
1.8%
6886
 
1.4%
12838
 
0.6%
01966
 
0.4%
21676
 
0.3%
9976
 
0.2%
6780
 
0.2%
3320
 
0.1%
Other values (4)403
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5195683
99.3%
None30568
 
0.6%
Punctuation6886
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A940260
18.1%
459844
 
8.9%
I399434
 
7.7%
L365169
 
7.0%
E364565
 
7.0%
R306341
 
5.9%
O281371
 
5.4%
C266208
 
5.1%
S246214
 
4.7%
N235783
 
4.5%
Other values (29)1330494
25.6%
Punctuation
ValueCountFrequency (%)
6886
100.0%
None
ValueCountFrequency (%)
Ó6308
20.6%
Í6204
20.3%
Á5861
19.2%
É4751
15.5%
Ñ3914
12.8%
Ú3210
10.5%
º320
 
1.0%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
JUZGADO DE FAMILIA
280447 
OTROS
92706 
JUZGADO DE PAZ
 
10855
FISCALÍA PENAL
 
10838
UNIDAD PNP
 
6802
Other values (4)
 
4783

Length

Max length26
Median length18
Mean length14.67696608
Min length5

Characters and Unicode

Total characters5965174
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJUZGADO DE FAMILIA
2nd rowJUZGADO DE FAMILIA
3rd rowOTROS
4th rowOTROS
5th rowJUZGADO DE FAMILIA

Common Values

ValueCountFrequency (%)
JUZGADO DE FAMILIA280447
69.0%
OTROS92706
 
22.8%
JUZGADO DE PAZ10855
 
2.7%
FISCALÍA PENAL10838
 
2.7%
UNIDAD PNP6802
 
1.7%
FISCALÍA DE FAMILIA3210
 
0.8%
JUZGADO PENAL1538
 
0.4%
FISCALIA DE MEDIO AMBIENTE32
 
< 0.1%
FISCALIA DE MEDIO AM3
 
< 0.1%

Length

2022-08-07T18:49:51.603101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:51.729101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
de294547
29.0%
juzgado292840
28.9%
familia283657
28.0%
otros92706
 
9.1%
fiscalía14048
 
1.4%
penal12376
 
1.2%
paz10855
 
1.1%
unidad6802
 
0.7%
pnp6802
 
0.7%
fiscalia35
 
< 0.1%
Other values (3)70
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A918388
15.4%
608307
10.2%
D601026
10.1%
I588301
9.9%
O478287
 
8.0%
L310116
 
5.2%
E307022
 
5.1%
Z303695
 
5.1%
U299642
 
5.0%
F297740
 
5.0%
Other values (11)1252650
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5356867
89.8%
Space Separator608307
 
10.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A918388
17.1%
D601026
11.2%
I588301
11.0%
O478287
8.9%
L310116
 
5.8%
E307022
 
5.7%
Z303695
 
5.7%
U299642
 
5.6%
F297740
 
5.6%
J292840
 
5.5%
Other values (10)959810
17.9%
Space Separator
ValueCountFrequency (%)
608307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5356867
89.8%
Common608307
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A918388
17.1%
D601026
11.2%
I588301
11.0%
O478287
8.9%
L310116
 
5.8%
E307022
 
5.7%
Z303695
 
5.7%
U299642
 
5.6%
F297740
 
5.6%
J292840
 
5.5%
Other values (10)959810
17.9%
Common
ValueCountFrequency (%)
608307
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5951126
99.8%
None14048
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A918388
15.4%
608307
10.2%
D601026
10.1%
I588301
9.9%
O478287
 
8.0%
L310116
 
5.2%
E307022
 
5.2%
Z303695
 
5.1%
U299642
 
5.0%
F297740
 
5.0%
Other values (10)1238602
20.8%
None
ValueCountFrequency (%)
Í14048
100.0%

DIRECCION
Categorical

HIGH CARDINALITY

Distinct1627
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
AV : BUEN PASTOR
 
4519
calle las gemas s/N la huayrona sjl
 
3949
AV. CESAR VALLEJO CUADRA 8
 
3893
Av. NICOLAS AYLLON S/N CARRETERA CENTRAL KM 7.5 VITARTE
 
3861
JR LIBERTAD 1200
 
3823
Other values (1622)
386386 

Length

Max length86
Median length57
Mean length25.26514956
Min length2

Characters and Unicode

Total characters10268540
Distinct characters92
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)< 0.1%

Sample

1st rowAV. LA RIVERA PLAZA SAN MARTIN
2nd rowCALLAO
3rd rowMz. Q S/N Ovalo Principal de Jicamarca
4th rowAV. PANAMERICA SUR SN
5th rowJJ ELIAS S/N

Common Values

ValueCountFrequency (%)
AV : BUEN PASTOR4519
 
1.1%
calle las gemas s/N la huayrona sjl3949
 
1.0%
AV. CESAR VALLEJO CUADRA 83893
 
1.0%
Av. NICOLAS AYLLON S/N CARRETERA CENTRAL KM 7.5 VITARTE3861
 
0.9%
JR LIBERTAD 12003823
 
0.9%
JR. SALVADOR3743
 
0.9%
CALLAO3731
 
0.9%
CALLE MONITOR HUASCAR2962
 
0.7%
AV. LIBERTAD 384-ICA2961
 
0.7%
AV MARISCAL CASTILLA CDRA 92865
 
0.7%
Other values (1617)370124
91.1%

Length

2022-08-07T18:49:51.877102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
av147155
 
7.9%
s/n109376
 
5.9%
jr88673
 
4.8%
calle62167
 
3.3%
de45984
 
2.5%
36537
 
2.0%
cdra21817
 
1.2%
san19516
 
1.1%
cuadra18891
 
1.0%
urb18649
 
1.0%
Other values (1885)1288475
69.4%

Most occurring characters

ValueCountFrequency (%)
1464659
 
14.3%
A1071826
 
10.4%
R544132
 
5.3%
L435079
 
4.2%
E435047
 
4.2%
N420714
 
4.1%
C380644
 
3.7%
O371646
 
3.6%
S350130
 
3.4%
a322183
 
3.1%
Other values (82)4472480
43.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6038827
58.8%
Lowercase Letter1667088
 
16.2%
Space Separator1464659
 
14.3%
Decimal Number583942
 
5.7%
Other Punctuation407709
 
4.0%
Dash Punctuation77778
 
0.8%
Other Symbol20607
 
0.2%
Other Letter6330
 
0.1%
Open Punctuation637
 
< 0.1%
Close Punctuation637
 
< 0.1%
Other values (3)326
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1071826
17.7%
R544132
 
9.0%
L435079
 
7.2%
E435047
 
7.2%
N420714
 
7.0%
C380644
 
6.3%
O371646
 
6.2%
S350130
 
5.8%
I320215
 
5.3%
U228321
 
3.8%
Other values (21)1481073
24.5%
Lowercase Letter
ValueCountFrequency (%)
a322183
19.3%
l150038
 
9.0%
r140829
 
8.4%
e120221
 
7.2%
c108824
 
6.5%
n103886
 
6.2%
o97356
 
5.8%
i95069
 
5.7%
s93359
 
5.6%
m58408
 
3.5%
Other values (21)376915
22.6%
Decimal Number
ValueCountFrequency (%)
1130056
22.3%
097987
16.8%
276150
13.0%
356238
9.6%
551348
 
8.8%
444750
 
7.7%
844718
 
7.7%
729008
 
5.0%
927615
 
4.7%
626072
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.275946
67.7%
/117829
28.9%
,5637
 
1.4%
:5179
 
1.3%
"1644
 
0.4%
#815
 
0.2%
;625
 
0.2%
'34
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-77437
99.6%
341
 
0.4%
Other Letter
ValueCountFrequency (%)
º6215
98.2%
ª115
 
1.8%
Math Symbol
ValueCountFrequency (%)
|162
97.6%
+4
 
2.4%
Space Separator
ValueCountFrequency (%)
1464659
100.0%
Other Symbol
ValueCountFrequency (%)
°20607
100.0%
Open Punctuation
ValueCountFrequency (%)
(637
100.0%
Close Punctuation
ValueCountFrequency (%)
)637
100.0%
Initial Punctuation
ValueCountFrequency (%)
80
100.0%
Final Punctuation
ValueCountFrequency (%)
80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7712245
75.1%
Common2556295
 
24.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1071826
 
13.9%
R544132
 
7.1%
L435079
 
5.6%
E435047
 
5.6%
N420714
 
5.5%
C380644
 
4.9%
O371646
 
4.8%
S350130
 
4.5%
a322183
 
4.2%
I320215
 
4.2%
Other values (54)3060629
39.7%
Common
ValueCountFrequency (%)
1464659
57.3%
.275946
 
10.8%
1130056
 
5.1%
/117829
 
4.6%
097987
 
3.8%
-77437
 
3.0%
276150
 
3.0%
356238
 
2.2%
551348
 
2.0%
444750
 
1.8%
Other values (18)163895
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10227725
99.6%
None40314
 
0.4%
Punctuation501
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1464659
 
14.3%
A1071826
 
10.5%
R544132
 
5.3%
L435079
 
4.3%
E435047
 
4.3%
N420714
 
4.1%
C380644
 
3.7%
O371646
 
3.6%
S350130
 
3.4%
a322183
 
3.2%
Other values (66)4431665
43.3%
None
ValueCountFrequency (%)
°20607
51.1%
Ñ9334
23.2%
º6215
 
15.4%
ñ1855
 
4.6%
ó1308
 
3.2%
é259
 
0.6%
í212
 
0.5%
Ó203
 
0.5%
ª115
 
0.3%
Á91
 
0.2%
Other values (3)115
 
0.3%
Punctuation
ValueCountFrequency (%)
341
68.1%
80
 
16.0%
80
 
16.0%

DIST_CIA
Categorical

HIGH CARDINALITY

Distinct791
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
SAN JUAN DE LURIGANCHO
 
15738
ATE
 
10607
LIMA
 
10398
CALLAO
 
10120
LOS OLIVOS
 
9350
Other values (786)
350218 

Length

Max length36
Median length25
Mean length10.06593985
Min length3

Characters and Unicode

Total characters4091110
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowHUANCHACO
2nd rowCALLAO
3rd rowLURIGANCHO - CHOSICA
4th rowOCOÑA
5th rowPARCONA

Common Values

ValueCountFrequency (%)
SAN JUAN DE LURIGANCHO15738
 
3.9%
ATE10607
 
2.6%
LIMA10398
 
2.6%
CALLAO10120
 
2.5%
LOS OLIVOS9350
 
2.3%
COMAS7486
 
1.8%
CHICLAYO7266
 
1.8%
PIURA6808
 
1.7%
VILLA MARIA DEL TRIUNFO6017
 
1.5%
VILLA EL SALVADOR6007
 
1.5%
Other values (781)316634
77.9%

Length

2022-08-07T18:49:52.011102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san40640
 
6.1%
de38458
 
5.8%
juan21227
 
3.2%
lurigancho18886
 
2.9%
el15553
 
2.4%
la12442
 
1.9%
villa12227
 
1.8%
ate10607
 
1.6%
lima10398
 
1.6%
los10387
 
1.6%
Other values (827)470861
71.2%

Most occurring characters

ValueCountFrequency (%)
A692766
16.9%
O314361
 
7.7%
L299061
 
7.3%
I262891
 
6.4%
C255290
 
6.2%
255255
 
6.2%
R253507
 
6.2%
N250546
 
6.1%
E245666
 
6.0%
S197953
 
4.8%
Other values (23)1063814
26.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3826773
93.5%
Space Separator255255
 
6.2%
Dash Punctuation4522
 
0.1%
Decimal Number4418
 
0.1%
Connector Punctuation114
 
< 0.1%
Other Punctuation28
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A692766
18.1%
O314361
 
8.2%
L299061
 
7.8%
I262891
 
6.9%
C255290
 
6.7%
R253507
 
6.6%
N250546
 
6.5%
E245666
 
6.4%
S197953
 
5.2%
U191615
 
5.0%
Other values (17)863117
22.6%
Decimal Number
ValueCountFrequency (%)
62209
50.0%
22209
50.0%
Space Separator
ValueCountFrequency (%)
255255
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4522
100.0%
Connector Punctuation
ValueCountFrequency (%)
_114
100.0%
Other Punctuation
ValueCountFrequency (%)
.28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3826773
93.5%
Common264337
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A692766
18.1%
O314361
 
8.2%
L299061
 
7.8%
I262891
 
6.9%
C255290
 
6.7%
R253507
 
6.6%
N250546
 
6.5%
E245666
 
6.4%
S197953
 
5.2%
U191615
 
5.0%
Other values (17)863117
22.6%
Common
ValueCountFrequency (%)
255255
96.6%
-4522
 
1.7%
62209
 
0.8%
22209
 
0.8%
_114
 
< 0.1%
.28
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4084540
99.8%
None6570
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A692766
17.0%
O314361
 
7.7%
L299061
 
7.3%
I262891
 
6.4%
C255290
 
6.3%
255255
 
6.2%
R253507
 
6.2%
N250546
 
6.1%
E245666
 
6.0%
S197953
 
4.8%
Other values (22)1057244
25.9%
None
ValueCountFrequency (%)
Ñ6570
100.0%

DIST_HECHO
Categorical

HIGH CARDINALITY

Distinct1331
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
SAN JUAN DE LURIGANCHO
 
15187
ATE
 
10377
CALLAO
 
9539
SAN MARTIN DE PORRES
 
8934
COMAS
 
8296
Other values (1326)
354098 

Length

Max length36
Median length27
Mean length10.35438734
Min length3

Characters and Unicode

Total characters4208344
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)< 0.1%

Sample

1st rowHUANCHACO
2nd rowCALLAO
3rd rowLURIGANCHO - CHOSICA
4th rowOCOÑA
5th rowPARCONA

Common Values

ValueCountFrequency (%)
SAN JUAN DE LURIGANCHO15187
 
3.7%
ATE10377
 
2.6%
CALLAO9539
 
2.3%
SAN MARTIN DE PORRES8934
 
2.2%
COMAS8296
 
2.0%
LIMA8267
 
2.0%
VILLA EL SALVADOR6923
 
1.7%
PIURA6891
 
1.7%
LOS OLIVOS6791
 
1.7%
VILLA MARIA DEL TRIUNFO6683
 
1.6%
Other values (1321)318543
78.4%

Length

2022-08-07T18:49:52.132102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san44352
 
6.5%
de41306
 
6.1%
juan22300
 
3.3%
lurigancho18739
 
2.7%
el16572
 
2.4%
la14180
 
2.1%
villa13809
 
2.0%
ate10377
 
1.5%
callao9539
 
1.4%
porres8934
 
1.3%
Other values (1355)482258
70.7%

Most occurring characters

ValueCountFrequency (%)
A710620
16.9%
O311354
 
7.4%
L298588
 
7.1%
275935
 
6.6%
R271204
 
6.4%
I266879
 
6.3%
N265641
 
6.3%
E261923
 
6.2%
C251896
 
6.0%
S205190
 
4.9%
Other values (31)1089114
25.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3922997
93.2%
Space Separator275935
 
6.6%
Dash Punctuation4945
 
0.1%
Decimal Number4258
 
0.1%
Connector Punctuation113
 
< 0.1%
Lowercase Letter60
 
< 0.1%
Other Punctuation36
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A710620
18.1%
O311354
 
7.9%
L298588
 
7.6%
R271204
 
6.9%
I266879
 
6.8%
N265641
 
6.8%
E261923
 
6.7%
C251896
 
6.4%
S205190
 
5.2%
U192683
 
4.9%
Other values (17)887019
22.6%
Lowercase Letter
ValueCountFrequency (%)
e15
25.0%
c10
16.7%
o10
16.7%
l5
 
8.3%
i5
 
8.3%
n5
 
8.3%
a5
 
8.3%
t5
 
8.3%
Decimal Number
ValueCountFrequency (%)
22129
50.0%
62129
50.0%
Space Separator
ValueCountFrequency (%)
275935
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4945
100.0%
Connector Punctuation
ValueCountFrequency (%)
_113
100.0%
Other Punctuation
ValueCountFrequency (%)
.36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3923057
93.2%
Common285287
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A710620
18.1%
O311354
 
7.9%
L298588
 
7.6%
R271204
 
6.9%
I266879
 
6.8%
N265641
 
6.8%
E261923
 
6.7%
C251896
 
6.4%
S205190
 
5.2%
U192683
 
4.9%
Other values (25)887079
22.6%
Common
ValueCountFrequency (%)
275935
96.7%
-4945
 
1.7%
22129
 
0.7%
62129
 
0.7%
_113
 
< 0.1%
.36
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4202141
99.9%
None6203
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A710620
16.9%
O311354
 
7.4%
L298588
 
7.1%
275935
 
6.6%
R271204
 
6.5%
I266879
 
6.4%
N265641
 
6.3%
E261923
 
6.2%
C251896
 
6.0%
S205190
 
4.9%
Other values (30)1082911
25.8%
None
ValueCountFrequency (%)
Ñ6203
100.0%

DPTO_CIA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
LIMA
150157 
AREQUIPA
37812 
PIURA
23054 
LA LIBERTAD
18826 
CUSCO
18718 
Other values (21)
157864 

Length

Max length13
Median length12
Mean length5.83654298
Min length3

Characters and Unicode

Total characters2372152
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA LIBERTAD
2nd rowCALLAO
3rd rowLIMA
4th rowAREQUIPA
5th rowICA

Common Values

ValueCountFrequency (%)
LIMA150157
36.9%
AREQUIPA37812
 
9.3%
PIURA23054
 
5.7%
LA LIBERTAD18826
 
4.6%
CUSCO18718
 
4.6%
CALLAO17907
 
4.4%
JUNIN17488
 
4.3%
LAMBAYEQUE17194
 
4.2%
ICA15576
 
3.8%
ANCASH14658
 
3.6%
Other values (16)75041
18.5%

Length

2022-08-07T18:49:52.242101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima150157
34.5%
arequipa37812
 
8.7%
piura23054
 
5.3%
la18826
 
4.3%
libertad18826
 
4.3%
cusco18718
 
4.3%
callao17907
 
4.1%
junin17488
 
4.0%
lambayeque17194
 
4.0%
ica15576
 
3.6%
Other values (20)99517
22.9%

Most occurring characters

ValueCountFrequency (%)
A528483
22.3%
I282067
11.9%
L251308
10.6%
M203118
 
8.6%
U164626
 
6.9%
C144961
 
6.1%
E110310
 
4.7%
R108193
 
4.6%
N87240
 
3.7%
O78658
 
3.3%
Other values (13)413188
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2343508
98.8%
Space Separator28644
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A528483
22.6%
I282067
12.0%
L251308
10.7%
M203118
 
8.7%
U164626
 
7.0%
C144961
 
6.2%
E110310
 
4.7%
R108193
 
4.6%
N87240
 
3.7%
O78658
 
3.4%
Other values (12)384544
16.4%
Space Separator
ValueCountFrequency (%)
28644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2343508
98.8%
Common28644
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A528483
22.6%
I282067
12.0%
L251308
10.7%
M203118
 
8.7%
U164626
 
7.0%
C144961
 
6.2%
E110310
 
4.7%
R108193
 
4.6%
N87240
 
3.7%
O78658
 
3.4%
Other values (12)384544
16.4%
Common
ValueCountFrequency (%)
28644
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2372152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A528483
22.3%
I282067
11.9%
L251308
10.6%
M203118
 
8.6%
U164626
 
6.9%
C144961
 
6.1%
E110310
 
4.7%
R108193
 
4.6%
N87240
 
3.7%
O78658
 
3.3%
Other values (13)413188
17.4%

DPTO_HECHO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
LIMA
149665 
AREQUIPA
38000 
PIURA
23158 
LA LIBERTAD
18791 
CUSCO
18694 
Other values (22)
158123 

Length

Max length13
Median length12
Mean length5.840460004
Min length3

Characters and Unicode

Total characters2373744
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA LIBERTAD
2nd rowCALLAO
3rd rowLIMA
4th rowAREQUIPA
5th rowICA

Common Values

ValueCountFrequency (%)
LIMA149665
36.8%
AREQUIPA38000
 
9.3%
PIURA23158
 
5.7%
LA LIBERTAD18791
 
4.6%
CUSCO18694
 
4.6%
CALLAO17875
 
4.4%
JUNIN17556
 
4.3%
LAMBAYEQUE17375
 
4.3%
ICA15587
 
3.8%
ANCASH14758
 
3.6%
Other values (17)74972
18.4%

Length

2022-08-07T18:49:52.344102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima149665
34.4%
arequipa38000
 
8.7%
piura23158
 
5.3%
la18791
 
4.3%
libertad18791
 
4.3%
cusco18694
 
4.3%
callao17875
 
4.1%
junin17556
 
4.0%
lambayeque17375
 
4.0%
ica15587
 
3.6%
Other values (22)99594
22.9%

Most occurring characters

ValueCountFrequency (%)
A528786
22.3%
I281924
11.9%
L250801
10.6%
M202678
 
8.5%
U165099
 
7.0%
C145082
 
6.1%
E110731
 
4.7%
R108336
 
4.6%
N87529
 
3.7%
O78439
 
3.3%
Other values (13)414339
17.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2345089
98.8%
Space Separator28655
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A528786
22.5%
I281924
12.0%
L250801
10.7%
M202678
 
8.6%
U165099
 
7.0%
C145082
 
6.2%
E110731
 
4.7%
R108336
 
4.6%
N87529
 
3.7%
O78439
 
3.3%
Other values (12)385684
16.4%
Space Separator
ValueCountFrequency (%)
28655
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2345089
98.8%
Common28655
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A528786
22.5%
I281924
12.0%
L250801
10.7%
M202678
 
8.6%
U165099
 
7.0%
C145082
 
6.2%
E110731
 
4.7%
R108336
 
4.6%
N87529
 
3.7%
O78439
 
3.3%
Other values (12)385684
16.4%
Common
ValueCountFrequency (%)
28655
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2373744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A528786
22.3%
I281924
11.9%
L250801
10.6%
M202678
 
8.5%
U165099
 
7.0%
C145082
 
6.1%
E110731
 
4.7%
R108336
 
4.6%
N87529
 
3.7%
O78439
 
3.3%
Other values (13)414339
17.5%

EDAD
Real number (ℝ≥0)

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.54480096
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:52.611130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q127
median34
Q343
95-th percentile57
Maximum75
Range57
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.30389395
Coefficient of variation (CV)0.3180182091
Kurtosis0.1629935235
Mean35.54480096
Median Absolute Deviation (MAD)8
Skewness0.7227917953
Sum14446509
Variance127.7780183
MonotonicityNot monotonic
2022-08-07T18:49:52.720130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3127482
 
6.8%
3013452
 
3.3%
3213287
 
3.3%
2813284
 
3.3%
2913165
 
3.2%
3313042
 
3.2%
3413034
 
3.2%
2713014
 
3.2%
2612769
 
3.1%
3512647
 
3.1%
Other values (48)261255
64.3%
ValueCountFrequency (%)
187228
1.8%
198266
2.0%
209364
2.3%
2110213
2.5%
2211032
2.7%
2311754
2.9%
2412183
3.0%
2512280
3.0%
2612769
3.1%
2713014
3.2%
ValueCountFrequency (%)
75261
 
0.1%
74367
0.1%
73378
0.1%
72443
0.1%
71523
0.1%
70586
0.1%
69644
0.2%
68670
0.2%
67783
0.2%
66901
0.2%

EST_CIVIL
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
SOLTERO(A)
290832 
CASADO(A)
69121 
CONVIVIENTE
36425 
NO INDICA
 
5425
DIVORCIADO(A)
 
3430

Length

Max length13
Median length10
Mean length9.925628212
Min length8

Characters and Unicode

Total characters4034083
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONVIVIENTE
2nd rowSOLTERO(A)
3rd rowCONVIVIENTE
4th rowSOLTERO(A)
5th rowSOLTERO(A)

Common Values

ValueCountFrequency (%)
SOLTERO(A)290832
71.6%
CASADO(A)69121
 
17.0%
CONVIVIENTE36425
 
9.0%
NO INDICA5425
 
1.3%
DIVORCIADO(A)3430
 
0.8%
VIUDO(A)1198
 
0.3%

Length

2022-08-07T18:49:52.827098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:52.937099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
soltero(a290832
70.6%
casado(a69121
 
16.8%
conviviente36425
 
8.8%
no5425
 
1.3%
indica5425
 
1.3%
divorciado(a3430
 
0.8%
viudo(a1198
 
0.3%

Most occurring characters

ValueCountFrequency (%)
O700693
17.4%
A511678
12.7%
(364581
9.0%
)364581
9.0%
E363682
9.0%
S359953
8.9%
T327257
8.1%
R294262
7.3%
L290832
7.2%
C114401
 
2.8%
Other values (6)342163
8.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3299496
81.8%
Open Punctuation364581
 
9.0%
Close Punctuation364581
 
9.0%
Space Separator5425
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O700693
21.2%
A511678
15.5%
E363682
11.0%
S359953
10.9%
T327257
9.9%
R294262
8.9%
L290832
8.8%
C114401
 
3.5%
I91758
 
2.8%
N83700
 
2.5%
Other values (3)161280
 
4.9%
Open Punctuation
ValueCountFrequency (%)
(364581
100.0%
Close Punctuation
ValueCountFrequency (%)
)364581
100.0%
Space Separator
ValueCountFrequency (%)
5425
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3299496
81.8%
Common734587
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O700693
21.2%
A511678
15.5%
E363682
11.0%
S359953
10.9%
T327257
9.9%
R294262
8.9%
L290832
8.8%
C114401
 
3.5%
I91758
 
2.8%
N83700
 
2.5%
Other values (3)161280
 
4.9%
Common
ValueCountFrequency (%)
(364581
49.6%
)364581
49.6%
5425
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4034083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O700693
17.4%
A511678
12.7%
(364581
9.0%
)364581
9.0%
E363682
9.0%
S359953
8.9%
T327257
8.1%
R294262
7.3%
L290832
7.2%
C114401
 
2.8%
Other values (6)342163
8.5%

LIBRO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
[FAM] DENUNCIA VIOLENCIA FAMILIAR
233602 
[FAM] ACTA DE DENUNCIA VERBAL
98431 
[FAM] ACTA DE INTERVENCION
 
17699
[FAM] DENUNCIA ABANDONO Y RETIRO DE HOGAR
 
17059
[DEINPOL] DENUNCIA DIRECTA DELITO
 
12513
Other values (38)
27127 

Length

Max length57
Median length33
Mean length32.11721547
Min length24

Characters and Unicode

Total characters13053432
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row[DEINPOL] ACTA DE DENUNCIA VERBAL
2nd row[FAM] DENUNCIA VIOLENCIA FAMILIAR
3rd row[FAM] DENUNCIA VIOLENCIA FAMILIAR
4th row[FAM] DENUNCIA ABANDONO Y RETIRO DE HOGAR
5th row[FAM] ACTA DE DENUNCIA VERBAL

Common Values

ValueCountFrequency (%)
[FAM] DENUNCIA VIOLENCIA FAMILIAR233602
57.5%
[FAM] ACTA DE DENUNCIA VERBAL98431
24.2%
[FAM] ACTA DE INTERVENCION17699
 
4.4%
[FAM] DENUNCIA ABANDONO Y RETIRO DE HOGAR17059
 
4.2%
[DEINPOL] DENUNCIA DIRECTA DELITO12513
 
3.1%
[FAM] OCURRENCIA VIOLENCIA FAMILIAR12301
 
3.0%
[DEINPOL] ACTA DE DENUNCIA VERBAL5996
 
1.5%
[FAM] OCURRENCIA MENORES2384
 
0.6%
[FAM] DENUNCIA VIOLENCIA FAMILIAR - RESERVADA1365
 
0.3%
[FAM] OCURRENCIA DE CALLE - COMUN1053
 
0.3%
Other values (33)4028
 
1.0%

Length

2022-08-07T18:49:53.057129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fam385497
21.6%
denuncia370928
20.7%
violencia247268
13.8%
familiar247268
13.8%
de143931
 
8.0%
acta124018
 
6.9%
verbal105042
 
5.9%
deinpol20439
 
1.1%
intervencion18976
 
1.1%
y17145
 
1.0%
Other values (41)107620
 
6.0%

Most occurring characters

ValueCountFrequency (%)
A1938476
14.9%
I1479024
11.3%
1381775
10.6%
N1122725
 
8.6%
E997469
 
7.6%
C810949
 
6.2%
M638026
 
4.9%
L637539
 
4.9%
F632963
 
4.8%
D579960
 
4.4%
Other values (23)2834526
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10855379
83.2%
Space Separator1381775
 
10.6%
Open Punctuation406433
 
3.1%
Close Punctuation406433
 
3.1%
Dash Punctuation3180
 
< 0.1%
Other Punctuation143
 
< 0.1%
Decimal Number89
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1938476
17.9%
I1479024
13.6%
N1122725
10.3%
E997469
9.2%
C810949
7.5%
M638026
 
5.9%
L637539
 
5.9%
F632963
 
5.8%
D579960
 
5.3%
R477147
 
4.4%
Other values (13)1541101
14.2%
Other Punctuation
ValueCountFrequency (%)
/89
62.2%
,53
37.1%
.1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
[406431
> 99.9%
(2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
]406431
> 99.9%
)2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1381775
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3180
100.0%
Decimal Number
ValueCountFrequency (%)
089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10855379
83.2%
Common2198053
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1938476
17.9%
I1479024
13.6%
N1122725
10.3%
E997469
9.2%
C810949
7.5%
M638026
 
5.9%
L637539
 
5.9%
F632963
 
5.8%
D579960
 
5.3%
R477147
 
4.4%
Other values (13)1541101
14.2%
Common
ValueCountFrequency (%)
1381775
62.9%
[406431
 
18.5%
]406431
 
18.5%
-3180
 
0.1%
/89
 
< 0.1%
089
 
< 0.1%
,53
 
< 0.1%
(2
 
< 0.1%
)2
 
< 0.1%
.1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII13053432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1938476
14.9%
I1479024
11.3%
1381775
10.6%
N1122725
 
8.6%
E997469
 
7.6%
C810949
 
6.2%
M638026
 
4.9%
L637539
 
4.9%
F632963
 
4.8%
D579960
 
4.4%
Other values (23)2834526
21.7%

MODALIDAD
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
VIOLENCIA PSICOLOGICA
177852 
VIOLENCIA FISICA Y PSICOLOGICA
155965 
VIOLENCIA FISICA
64114 
VIOLENCIA ECONOMICA O PATRIMONIAL
 
6900
MALTRATO SIN LESION
 
1453
Other values (2)
 
147

Length

Max length33
Median length30
Mean length23.85872879
Min length13

Characters and Unicode

Total characters9696927
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALTRATO SIN LESION
2nd rowMALTRATO SIN LESION
3rd rowMALTRATO SIN LESION
4th rowVIOLENCIA PSICOLOGICA
5th rowVIOLENCIA FISICA

Common Values

ValueCountFrequency (%)
VIOLENCIA PSICOLOGICA177852
43.8%
VIOLENCIA FISICA Y PSICOLOGICA155965
38.4%
VIOLENCIA FISICA64114
 
15.8%
VIOLENCIA ECONOMICA O PATRIMONIAL6900
 
1.7%
MALTRATO SIN LESION1453
 
0.4%
AMENAZA GRAVE104
 
< 0.1%
COACCION GRAVE43
 
< 0.1%

Length

2022-08-07T18:49:53.164099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:53.287099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
violencia404831
35.5%
psicologica333817
29.3%
fisica220079
19.3%
y155965
 
13.7%
economica6900
 
0.6%
o6900
 
0.6%
patrimonial6900
 
0.6%
maltrato1453
 
0.1%
sin1453
 
0.1%
lesion1453
 
0.1%
Other values (3)294
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I1941103
20.0%
C1306473
13.5%
O1103057
11.4%
A982835
10.1%
L748454
 
7.7%
733614
 
7.6%
S556802
 
5.7%
N421684
 
4.3%
E413435
 
4.3%
V404978
 
4.2%
Other values (8)1084492
11.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8963313
92.4%
Space Separator733614
 
7.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I1941103
21.7%
C1306473
14.6%
O1103057
12.3%
A982835
11.0%
L748454
 
8.4%
S556802
 
6.2%
N421684
 
4.7%
E413435
 
4.6%
V404978
 
4.5%
P340717
 
3.8%
Other values (7)743775
 
8.3%
Space Separator
ValueCountFrequency (%)
733614
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8963313
92.4%
Common733614
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
I1941103
21.7%
C1306473
14.6%
O1103057
12.3%
A982835
11.0%
L748454
 
8.4%
S556802
 
6.2%
N421684
 
4.7%
E413435
 
4.6%
V404978
 
4.5%
P340717
 
3.8%
Other values (7)743775
 
8.3%
Common
ValueCountFrequency (%)
733614
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9696927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I1941103
20.0%
C1306473
13.5%
O1103057
11.4%
A982835
10.1%
L748454
 
7.7%
733614
 
7.6%
S556802
 
5.7%
N421684
 
4.3%
E413435
 
4.3%
V404978
 
4.2%
Other values (8)1084492
11.2%

PROV_CIA
Categorical

HIGH CARDINALITY

Distinct187
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
LIMA
139054 
AREQUIPA
31641 
CALLAO
 
17907
TRUJILLO
 
13707
CHICLAYO
 
13542
Other values (182)
190580 

Length

Max length25
Median length23
Mean length6.1567843
Min length3

Characters and Unicode

Total characters2502308
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTRUJILLO
2nd rowCALLAO
3rd rowLIMA
4th rowCAMANA
5th rowICA

Common Values

ValueCountFrequency (%)
LIMA139054
34.2%
AREQUIPA31641
 
7.8%
CALLAO17907
 
4.4%
TRUJILLO13707
 
3.4%
CHICLAYO13542
 
3.3%
PIURA13135
 
3.2%
CUSCO10650
 
2.6%
HUANCAYO9899
 
2.4%
ICA7504
 
1.8%
TACNA7363
 
1.8%
Other values (177)142029
34.9%

Length

2022-08-07T18:49:53.416102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima139054
32.5%
arequipa31641
 
7.4%
callao17907
 
4.2%
trujillo13707
 
3.2%
chiclayo13542
 
3.2%
piura13135
 
3.1%
cusco10650
 
2.5%
huancayo9899
 
2.3%
ica7504
 
1.8%
santa7428
 
1.7%
Other values (203)163804
38.2%

Most occurring characters

ValueCountFrequency (%)
A558504
22.3%
L272244
10.9%
I264410
10.6%
M189436
 
7.6%
C182641
 
7.3%
U140325
 
5.6%
O139730
 
5.6%
R114983
 
4.6%
N105061
 
4.2%
E76667
 
3.1%
Other values (17)458307
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2480468
99.1%
Space Separator21840
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A558504
22.5%
L272244
11.0%
I264410
10.7%
M189436
 
7.6%
C182641
 
7.4%
U140325
 
5.7%
O139730
 
5.6%
R114983
 
4.6%
N105061
 
4.2%
E76667
 
3.1%
Other values (16)436467
17.6%
Space Separator
ValueCountFrequency (%)
21840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2480468
99.1%
Common21840
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A558504
22.5%
L272244
11.0%
I264410
10.7%
M189436
 
7.6%
C182641
 
7.4%
U140325
 
5.7%
O139730
 
5.6%
R114983
 
4.6%
N105061
 
4.2%
E76667
 
3.1%
Other values (16)436467
17.6%
Common
ValueCountFrequency (%)
21840
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2497669
99.8%
None4639
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A558504
22.4%
L272244
10.9%
I264410
10.6%
M189436
 
7.6%
C182641
 
7.3%
U140325
 
5.6%
O139730
 
5.6%
R114983
 
4.6%
N105061
 
4.2%
E76667
 
3.1%
Other values (16)453668
18.2%
None
ValueCountFrequency (%)
Ñ4639
100.0%

PROV_HECHO
Categorical

HIGH CARDINALITY

Distinct190
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
LIMA
138456 
AREQUIPA
31939 
CALLAO
 
17875
TRUJILLO
 
14371
CHICLAYO
 
13688
Other values (185)
190102 

Length

Max length25
Median length23
Mean length6.164104116
Min length3

Characters and Unicode

Total characters2505283
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRUJILLO
2nd rowCALLAO
3rd rowLIMA
4th rowCAMANA
5th rowICA

Common Values

ValueCountFrequency (%)
LIMA138456
34.1%
AREQUIPA31939
 
7.9%
CALLAO17875
 
4.4%
TRUJILLO14371
 
3.5%
CHICLAYO13688
 
3.4%
PIURA13205
 
3.2%
CUSCO10486
 
2.6%
HUANCAYO10274
 
2.5%
ICA7459
 
1.8%
TACNA7382
 
1.8%
Other values (180)141296
34.8%

Length

2022-08-07T18:49:53.528102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima138456
32.3%
arequipa31939
 
7.5%
callao17875
 
4.2%
trujillo14371
 
3.4%
chiclayo13688
 
3.2%
piura13205
 
3.1%
cusco10486
 
2.4%
huancayo10274
 
2.4%
ica7459
 
1.7%
santa7414
 
1.7%
Other values (208)163022
38.1%

Most occurring characters

ValueCountFrequency (%)
A558193
22.3%
L274568
11.0%
I265044
10.6%
M188148
 
7.5%
C181271
 
7.2%
U141344
 
5.6%
O139873
 
5.6%
R116087
 
4.6%
N104909
 
4.2%
E76620
 
3.1%
Other values (25)459226
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2483465
99.1%
Space Separator21758
 
0.9%
Lowercase Letter60
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A558193
22.5%
L274568
11.1%
I265044
10.7%
M188148
 
7.6%
C181271
 
7.3%
U141344
 
5.7%
O139873
 
5.6%
R116087
 
4.7%
N104909
 
4.2%
E76620
 
3.1%
Other values (16)437408
17.6%
Lowercase Letter
ValueCountFrequency (%)
e15
25.0%
c10
16.7%
o10
16.7%
l5
 
8.3%
i5
 
8.3%
n5
 
8.3%
a5
 
8.3%
t5
 
8.3%
Space Separator
ValueCountFrequency (%)
21758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2483525
99.1%
Common21758
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A558193
22.5%
L274568
11.1%
I265044
10.7%
M188148
 
7.6%
C181271
 
7.3%
U141344
 
5.7%
O139873
 
5.6%
R116087
 
4.7%
N104909
 
4.2%
E76620
 
3.1%
Other values (24)437468
17.6%
Common
ValueCountFrequency (%)
21758
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2500665
99.8%
None4618
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A558193
22.3%
L274568
11.0%
I265044
10.6%
M188148
 
7.5%
C181271
 
7.2%
U141344
 
5.7%
O139873
 
5.6%
R116087
 
4.6%
N104909
 
4.2%
E76620
 
3.1%
Other values (24)454608
18.2%
None
ValueCountFrequency (%)
Ñ4618
100.0%

REGION
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
REGPOL - LIMA
105170 
REGPOL - LIMA
39570 
REGPOL - AREQUIPA
28304 
REGPOL - PIURA
 
15649
REGPOL - LA LIBERTAD
 
13346
Other values (49)
204392 

Length

Max length32
Median length23
Mean length16.44554918
Min length6

Characters and Unicode

Total characters6683981
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREGPOL - LA LIBERTAD
2nd rowREGPOL - CALLAO
3rd rowREGPOL - LIMA
4th rowREGPOL - AREQUIPA
5th rowREGPOL - ICA

Common Values

ValueCountFrequency (%)
REGPOL - LIMA105170
25.9%
REGPOL - LIMA39570
 
9.7%
REGPOL - AREQUIPA28304
 
7.0%
REGPOL - PIURA15649
 
3.9%
REGPOL - LA LIBERTAD13346
 
3.3%
REGPOL - CALLAO13118
 
3.2%
REGPOL - LAMBAYEQUE12519
 
3.1%
REGPOL - ICA11230
 
2.8%
REGPOL - HUANCAYO11198
 
2.8%
REGPOL - CUSCO11113
 
2.7%
Other values (44)145214
35.7%

Length

2022-08-07T18:49:53.648098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
395444
31.6%
regpol386142
30.9%
lima144740
 
11.6%
arequipa38029
 
3.0%
piura23168
 
1.9%
la18782
 
1.5%
libertad18782
 
1.5%
callao18018
 
1.4%
lambayeque17352
 
1.4%
huancayo16684
 
1.3%
Other values (31)172904
13.8%

Most occurring characters

ValueCountFrequency (%)
1375912
20.6%
L636705
9.5%
A583806
8.7%
R519994
 
7.8%
E511545
 
7.7%
P488031
 
7.3%
O480572
 
7.2%
-395444
 
5.9%
G394956
 
5.9%
I301053
 
4.5%
Other values (15)995963
14.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4912625
73.5%
Space Separator1375912
 
20.6%
Dash Punctuation395444
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L636705
13.0%
A583806
11.9%
R519994
10.6%
E511545
10.4%
P488031
9.9%
O480572
9.8%
G394956
8.0%
I301053
6.1%
M201125
 
4.1%
U188074
 
3.8%
Other values (13)606764
12.4%
Space Separator
ValueCountFrequency (%)
1375912
100.0%
Dash Punctuation
ValueCountFrequency (%)
-395444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4912625
73.5%
Common1771356
 
26.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
L636705
13.0%
A583806
11.9%
R519994
10.6%
E511545
10.4%
P488031
9.9%
O480572
9.8%
G394956
8.0%
I301053
6.1%
M201125
 
4.1%
U188074
 
3.8%
Other values (13)606764
12.4%
Common
ValueCountFrequency (%)
1375912
77.7%
-395444
 
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6683981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1375912
20.6%
L636705
9.5%
A583806
8.7%
R519994
 
7.8%
E511545
 
7.7%
P488031
 
7.3%
O480572
 
7.2%
-395444
 
5.9%
G394956
 
5.9%
I301053
 
4.5%
Other values (15)995963
14.9%

SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
M
327791 
F
78640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters406431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M327791
80.7%
F78640
 
19.3%

Length

2022-08-07T18:49:53.760098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:53.857102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
m327791
80.7%
f78640
 
19.3%

Most occurring characters

ValueCountFrequency (%)
M327791
80.7%
F78640
 
19.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter406431
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M327791
80.7%
F78640
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
Latin406431
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M327791
80.7%
F78640
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII406431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M327791
80.7%
F78640
 
19.3%

SUB_TIPO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
LEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)
404831 
LEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
 
1600

Length

Max length126
Median length126
Mean length125.7637975
Min length66

Characters and Unicode

Total characters51114306
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
2nd rowLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
3rd rowLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
4th rowLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)
5th rowLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)

Common Values

ValueCountFrequency (%)
LEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)404831
99.6%
LEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)1600
 
0.4%

Length

2022-08-07T18:49:53.944129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:54.045102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ley812862
 
9.5%
y809662
 
9.5%
familiar406431
 
4.8%
violencia406431
 
4.8%
mujeres404831
 
4.8%
30364404831
 
4.8%
nro404831
 
4.8%
grupo404831
 
4.8%
del404831
 
4.8%
integrantes404831
 
4.8%
Other values (15)3653079
42.9%

Most occurring characters

ValueCountFrequency (%)
8111020
15.9%
A5269203
10.3%
R4862772
 
9.5%
E4464341
 
8.7%
I3246648
 
6.4%
L3245048
 
6.3%
N3243448
 
6.3%
O2027355
 
4.0%
S2024155
 
4.0%
C1624124
 
3.2%
Other values (25)12996192
25.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter38930976
76.2%
Space Separator8111020
 
15.9%
Decimal Number2041755
 
4.0%
Lowercase Letter809662
 
1.6%
Other Punctuation408031
 
0.8%
Open Punctuation406431
 
0.8%
Close Punctuation406431
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A5269203
13.5%
R4862772
12.5%
E4464341
11.5%
I3246648
8.3%
L3245048
8.3%
N3243448
8.3%
O2027355
 
5.2%
S2024155
 
5.2%
C1624124
 
4.2%
Y1622524
 
4.2%
Other values (10)7301358
18.8%
Decimal Number
ValueCountFrequency (%)
3809662
39.7%
6409631
20.1%
0408031
20.0%
4404831
19.8%
24800
 
0.2%
51600
 
0.1%
91600
 
0.1%
71600
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
r404831
50.0%
o404831
50.0%
Other Punctuation
ValueCountFrequency (%)
,404831
99.2%
/3200
 
0.8%
Space Separator
ValueCountFrequency (%)
8111020
100.0%
Open Punctuation
ValueCountFrequency (%)
(406431
100.0%
Close Punctuation
ValueCountFrequency (%)
)406431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39740638
77.7%
Common11373668
 
22.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A5269203
13.3%
R4862772
12.2%
E4464341
11.2%
I3246648
8.2%
L3245048
8.2%
N3243448
 
8.2%
O2027355
 
5.1%
S2024155
 
5.1%
C1624124
 
4.1%
Y1622524
 
4.1%
Other values (12)8111020
20.4%
Common
ValueCountFrequency (%)
8111020
71.3%
3809662
 
7.1%
6409631
 
3.6%
0408031
 
3.6%
(406431
 
3.6%
)406431
 
3.6%
,404831
 
3.6%
4404831
 
3.6%
24800
 
< 0.1%
/3200
 
< 0.1%
Other values (3)4800
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII51112706
> 99.9%
None1600
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8111020
15.9%
A5269203
10.3%
R4862772
 
9.5%
E4464341
 
8.7%
I3246648
 
6.4%
L3245048
 
6.3%
N3243448
 
6.3%
O2027355
 
4.0%
S2024155
 
4.0%
C1624124
 
3.2%
Other values (24)12994592
25.4%
None
ValueCountFrequency (%)
Ó1600
100.0%

TIPO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
VIOLENCIA FAMILIAR
262187 
LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLES
144244 

Length

Max length53
Median length18
Mean length30.42164107
Min length18

Characters and Unicode

Total characters12364298
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVIOLENCIA FAMILIAR
2nd rowVIOLENCIA FAMILIAR
3rd rowVIOLENCIA FAMILIAR
4th rowVIOLENCIA FAMILIAR
5th rowVIOLENCIA FAMILIAR

Common Values

ValueCountFrequency (%)
VIOLENCIA FAMILIAR262187
64.5%
LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLES144244
35.5%

Length

2022-08-07T18:49:54.146098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:54.242102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
violencia406431
22.3%
familiar262187
14.4%
ley144244
 
7.9%
de144244
 
7.9%
contra144244
 
7.9%
la144244
 
7.9%
mujer144244
 
7.9%
y144244
 
7.9%
grupos144244
 
7.9%
vulnerables144244
 
7.9%

Most occurring characters

ValueCountFrequency (%)
1416139
11.5%
A1363537
11.0%
I1337236
10.8%
L1245594
10.1%
E1127651
9.1%
R839163
 
6.8%
O694919
 
5.6%
N694919
 
5.6%
V550675
 
4.5%
C550675
 
4.5%
Other values (11)2543790
20.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10948159
88.5%
Space Separator1416139
 
11.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1363537
12.5%
I1337236
12.2%
L1245594
11.4%
E1127651
10.3%
R839163
 
7.7%
O694919
 
6.3%
N694919
 
6.3%
V550675
 
5.0%
C550675
 
5.0%
U432732
 
4.0%
Other values (10)2111058
19.3%
Space Separator
ValueCountFrequency (%)
1416139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10948159
88.5%
Common1416139
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1363537
12.5%
I1337236
12.2%
L1245594
11.4%
E1127651
10.3%
R839163
 
7.7%
O694919
 
6.3%
N694919
 
6.3%
V550675
 
5.0%
C550675
 
5.0%
U432732
 
4.0%
Other values (10)2111058
19.3%
Common
ValueCountFrequency (%)
1416139
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12364298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1416139
11.5%
A1363537
11.0%
I1337236
10.8%
L1245594
10.1%
E1127651
9.1%
R839163
 
6.8%
O694919
 
5.6%
N694919
 
5.6%
V550675
 
4.5%
C550675
 
4.5%
Other values (11)2543790
20.6%

TIPO_DENUNCIA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
DENUNCIA
265750 
ACTA DE DENUNCIA VERBAL
105177 
ACTA DE INTERVENCION
 
18979
OCURRENCIA
 
16525

Length

Max length23
Median length8
Mean length12.52340742
Min length8

Characters and Unicode

Total characters5089901
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTA DE DENUNCIA VERBAL
2nd rowDENUNCIA
3rd rowDENUNCIA
4th rowDENUNCIA
5th rowACTA DE DENUNCIA VERBAL

Common Values

ValueCountFrequency (%)
DENUNCIA265750
65.4%
ACTA DE DENUNCIA VERBAL105177
 
25.9%
ACTA DE INTERVENCION18979
 
4.7%
OCURRENCIA16525
 
4.1%

Length

2022-08-07T18:49:54.337099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:54.443128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
denuncia370927
48.8%
acta124156
 
16.3%
de124156
 
16.3%
verbal105177
 
13.8%
intervencion18979
 
2.5%
ocurrencia16525
 
2.2%

Most occurring characters

ValueCountFrequency (%)
N815316
16.0%
A740941
14.6%
E654743
12.9%
C547112
10.7%
D495083
9.7%
I425410
8.4%
U387452
7.6%
353489
6.9%
R157206
 
3.1%
T143135
 
2.8%
Other values (4)370014
7.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4736412
93.1%
Space Separator353489
 
6.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N815316
17.2%
A740941
15.6%
E654743
13.8%
C547112
11.6%
D495083
10.5%
I425410
9.0%
U387452
8.2%
R157206
 
3.3%
T143135
 
3.0%
V124156
 
2.6%
Other values (3)245858
 
5.2%
Space Separator
ValueCountFrequency (%)
353489
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4736412
93.1%
Common353489
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N815316
17.2%
A740941
15.6%
E654743
13.8%
C547112
11.6%
D495083
10.5%
I425410
9.0%
U387452
8.2%
R157206
 
3.3%
T143135
 
3.0%
V124156
 
2.6%
Other values (3)245858
 
5.2%
Common
ValueCountFrequency (%)
353489
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5089901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N815316
16.0%
A740941
14.6%
E654743
12.9%
C547112
10.7%
D495083
9.7%
I425410
8.4%
U387452
7.6%
353489
6.9%
R157206
 
3.1%
T143135
 
2.8%
Other values (4)370014
7.3%

UBICACION
Categorical

HIGH CARDINALITY

Distinct379092
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Unnamed Road, Perú
 
156
CALLE
 
141
SAN MARTIN
 
134
VILLA MARIA DEL TRIUNFO
 
109
VIA PUBLICA
 
104
Other values (379087)
405787 

Length

Max length100
Median length78
Mean length36.73316504
Min length1

Characters and Unicode

Total characters14929497
Distinct characters148
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique367815 ?
Unique (%)90.5%

Sample

1st rowSECTOR SANTA ROSA MZ. 11 LT. 36 - HUANCHAQUITO ALTO
2nd rowMZ. 2 LOTE 31
3rd rowSANTA CRUZ
4th rowAA. HH. Gilberto CARNERO Anexo de Planchada
5th rowEL PURGATORIO MZ- C LT 8 AV 7

Common Values

ValueCountFrequency (%)
Unnamed Road, Perú156
 
< 0.1%
CALLE141
 
< 0.1%
SAN MARTIN134
 
< 0.1%
VILLA MARIA DEL TRIUNFO109
 
< 0.1%
VIA PUBLICA104
 
< 0.1%
PACHACUTEC101
 
< 0.1%
PAYET- INDEPENDENCIA98
 
< 0.1%
SANTA ROSA98
 
< 0.1%
Cannot determine address at this location.95
 
< 0.1%
28 DE JULIO90
 
< 0.1%
Other values (379082)405305
99.7%

Length

2022-08-07T18:49:54.581101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mz106194
 
3.9%
de105250
 
3.8%
80494
 
2.9%
lote49458
 
1.8%
lt49406
 
1.8%
la43862
 
1.6%
calle41730
 
1.5%
av41465
 
1.5%
san38342
 
1.4%
los34341
 
1.3%
Other values (102065)2144619
78.4%

Most occurring characters

ValueCountFrequency (%)
2367249
 
15.9%
A1386060
 
9.3%
E776426
 
5.2%
O718818
 
4.8%
L711151
 
4.8%
R651120
 
4.4%
N546320
 
3.7%
I525777
 
3.5%
S504228
 
3.4%
C496525
 
3.3%
Other values (138)6245823
41.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9056443
60.7%
Space Separator2367637
 
15.9%
Lowercase Letter1824758
 
12.2%
Decimal Number874513
 
5.9%
Other Punctuation549419
 
3.7%
Dash Punctuation178506
 
1.2%
Other Symbol30539
 
0.2%
Open Punctuation16980
 
0.1%
Close Punctuation16085
 
0.1%
Other Letter8501
 
0.1%
Other values (7)6116
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1386060
15.3%
E776426
 
8.6%
O718818
 
7.9%
L711151
 
7.9%
R651120
 
7.2%
N546320
 
6.0%
I525777
 
5.8%
S504228
 
5.6%
C496525
 
5.5%
T430812
 
4.8%
Other values (35)2309206
25.5%
Lowercase Letter
ValueCountFrequency (%)
a283859
15.6%
e177192
9.7%
o156753
 
8.6%
r152101
 
8.3%
l140723
 
7.7%
i109729
 
6.0%
n107086
 
5.9%
s93153
 
5.1%
c90534
 
5.0%
t86559
 
4.7%
Other values (30)427069
23.4%
Other Punctuation
ValueCountFrequency (%)
.403428
73.4%
,61090
 
11.1%
/35293
 
6.4%
?29208
 
5.3%
"12517
 
2.3%
:6307
 
1.1%
#689
 
0.1%
;382
 
0.1%
¡246
 
< 0.1%
'175
 
< 0.1%
Other values (7)84
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1196756
22.5%
2129612
14.8%
0127168
14.5%
388027
10.1%
470706
 
8.1%
566768
 
7.6%
654431
 
6.2%
749817
 
5.7%
848877
 
5.6%
942351
 
4.8%
Math Symbol
ValueCountFrequency (%)
¬1729
80.6%
±250
 
11.7%
|133
 
6.2%
+22
 
1.0%
>4
 
0.2%
=3
 
0.1%
<3
 
0.1%
~1
 
< 0.1%
Other Number
ValueCountFrequency (%)
³247
82.1%
½39
 
13.0%
¹10
 
3.3%
¼3
 
1.0%
²2
 
0.7%
Modifier Symbol
ValueCountFrequency (%)
´210
76.1%
`41
 
14.9%
¨23
 
8.3%
^2
 
0.7%
Other Symbol
ValueCountFrequency (%)
°30414
99.6%
©115
 
0.4%
¦10
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)16076
99.9%
}7
 
< 0.1%
]2
 
< 0.1%
Control
ValueCountFrequency (%)
111
41.4%
94
35.1%
63
23.5%
Space Separator
ValueCountFrequency (%)
2367249
> 99.9%
 388
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(16975
> 99.9%
{5
 
< 0.1%
Other Letter
ValueCountFrequency (%)
º7525
88.5%
ª976
 
11.5%
Dash Punctuation
ValueCountFrequency (%)
-178506
100.0%
Currency Symbol
ValueCountFrequency (%)
¢2761
100.0%
Connector Punctuation
ValueCountFrequency (%)
_205
100.0%
Format
ValueCountFrequency (%)
­160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10889702
72.9%
Common4039795
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1386060
 
12.7%
E776426
 
7.1%
O718818
 
6.6%
L711151
 
6.5%
R651120
 
6.0%
N546320
 
5.0%
I525777
 
4.8%
S504228
 
4.6%
C496525
 
4.6%
T430812
 
4.0%
Other values (77)4142465
38.0%
Common
ValueCountFrequency (%)
2367249
58.6%
.403428
 
10.0%
1196756
 
4.9%
-178506
 
4.4%
2129612
 
3.2%
0127168
 
3.1%
388027
 
2.2%
470706
 
1.8%
566768
 
1.7%
,61090
 
1.5%
Other values (51)350485
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII14822205
99.3%
None107292
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2367249
 
16.0%
A1386060
 
9.4%
E776426
 
5.2%
O718818
 
4.8%
L711151
 
4.8%
R651120
 
4.4%
N546320
 
3.7%
I525777
 
3.5%
S504228
 
3.4%
C496525
 
3.3%
Other values (82)6138531
41.4%
None
ValueCountFrequency (%)
°30414
28.3%
Ã11415
 
10.6%
Ñ11382
 
10.6%
ú10869
 
10.1%
º7525
 
7.0%
Â7009
 
6.5%
â3699
 
3.4%
ó2854
 
2.7%
¢2761
 
2.6%
Ó2211
 
2.1%
Other values (46)17153
16.0%

VIA
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Otros
234895 
Avenida
62288 
Calle
34497 
Jiron
30260 
AA.HH
 
10550
Other values (11)
33941 

Length

Max length14
Median length5
Mean length5.681977999
Min length4

Characters and Unicode

Total characters2309332
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOtros
2nd rowOtros
3rd rowOtros
4th rowCarretera
5th rowCalle

Common Values

ValueCountFrequency (%)
Otros234895
57.8%
Avenida62288
 
15.3%
Calle34497
 
8.5%
Jiron30260
 
7.4%
AA.HH10550
 
2.6%
Pasaje8068
 
2.0%
Centro Poblado7644
 
1.9%
Urbanización6862
 
1.7%
Caserio3944
 
1.0%
Comunidad2850
 
0.7%
Other values (6)4573
 
1.1%

Length

2022-08-07T18:49:54.699103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
otros234895
56.7%
avenida62288
 
15.0%
calle34497
 
8.3%
jiron30260
 
7.3%
aa.hh10550
 
2.5%
pasaje8068
 
1.9%
centro7644
 
1.8%
poblado7644
 
1.8%
urbanización6862
 
1.7%
caserio3944
 
1.0%
Other values (7)7423
 
1.8%

Most occurring characters

ValueCountFrequency (%)
o296158
12.8%
r288376
12.5%
s247687
10.7%
t243751
10.6%
O234895
10.2%
a146248
 
6.3%
e121446
 
5.3%
n119170
 
5.2%
i114325
 
5.0%
A84665
 
3.7%
Other values (21)412611
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1845413
79.9%
Uppercase Letter445725
 
19.3%
Other Punctuation10550
 
0.5%
Space Separator7644
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o296158
16.0%
r288376
15.6%
s247687
13.4%
t243751
13.2%
a146248
7.9%
e121446
6.6%
n119170
6.5%
i114325
 
6.2%
l76807
 
4.2%
d75632
 
4.1%
Other values (11)115813
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
O234895
52.7%
A84665
 
19.0%
C50147
 
11.3%
J30260
 
6.8%
H21100
 
4.7%
P16847
 
3.8%
U7642
 
1.7%
M169
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.10550
100.0%
Space Separator
ValueCountFrequency (%)
7644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2291138
99.2%
Common18194
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o296158
12.9%
r288376
12.6%
s247687
10.8%
t243751
10.6%
O234895
10.3%
a146248
 
6.4%
e121446
 
5.3%
n119170
 
5.2%
i114325
 
5.0%
A84665
 
3.7%
Other values (19)394417
17.2%
Common
ValueCountFrequency (%)
.10550
58.0%
7644
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2302301
99.7%
None7031
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o296158
12.9%
r288376
12.5%
s247687
10.8%
t243751
10.6%
O234895
10.2%
a146248
 
6.4%
e121446
 
5.3%
n119170
 
5.2%
i114325
 
5.0%
A84665
 
3.7%
Other values (20)405580
17.6%
None
ValueCountFrequency (%)
ó7031
100.0%

PAIS_NATAL
Categorical

HIGH CARDINALITY

Distinct153
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
PERU
403298 
VENEZUELA
 
1246
VENEZOLANO(A)
 
660
COLOMBIA
 
200
ARGENTINA
 
105
Other values (148)
 
922

Length

Max length25
Median length4
Mean length4.041655287
Min length3

Characters and Unicode

Total characters1642654
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)< 0.1%

Sample

1st rowPERU
2nd rowPERU
3rd rowPERU
4th rowPERU
5th rowPERU

Common Values

ValueCountFrequency (%)
PERU403298
99.2%
VENEZUELA1246
 
0.3%
VENEZOLANO(A)660
 
0.2%
COLOMBIA200
 
< 0.1%
ARGENTINA105
 
< 0.1%
ECUADOR88
 
< 0.1%
CHILE76
 
< 0.1%
VENEZOLANO52
 
< 0.1%
BRASIL50
 
< 0.1%
ESPAÑA49
 
< 0.1%
Other values (143)607
 
0.1%

Length

2022-08-07T18:49:54.804102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peru403299
99.2%
venezuela1247
 
0.3%
venezolano(a660
 
0.2%
colombia200
 
< 0.1%
argentina105
 
< 0.1%
ecuador88
 
< 0.1%
chile79
 
< 0.1%
venezolano52
 
< 0.1%
brasil50
 
< 0.1%
españa50
 
< 0.1%
Other values (159)708
 
0.2%

Most occurring characters

ValueCountFrequency (%)
E409131
24.9%
U404909
24.6%
R403791
24.6%
P403415
24.6%
A4123
 
0.3%
N3251
 
0.2%
L2577
 
0.2%
O2319
 
0.1%
V2046
 
0.1%
Z1992
 
0.1%
Other values (20)5100
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1641217
99.9%
Close Punctuation660
 
< 0.1%
Open Punctuation660
 
< 0.1%
Space Separator117
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E409131
24.9%
U404909
24.7%
R403791
24.6%
P403415
24.6%
A4123
 
0.3%
N3251
 
0.2%
L2577
 
0.2%
O2319
 
0.1%
V2046
 
0.1%
Z1992
 
0.1%
Other values (17)3663
 
0.2%
Close Punctuation
ValueCountFrequency (%)
)660
100.0%
Open Punctuation
ValueCountFrequency (%)
(660
100.0%
Space Separator
ValueCountFrequency (%)
117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1641217
99.9%
Common1437
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E409131
24.9%
U404909
24.7%
R403791
24.6%
P403415
24.6%
A4123
 
0.3%
N3251
 
0.2%
L2577
 
0.2%
O2319
 
0.1%
V2046
 
0.1%
Z1992
 
0.1%
Other values (17)3663
 
0.2%
Common
ValueCountFrequency (%)
)660
45.9%
(660
45.9%
117
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1642590
> 99.9%
None64
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E409131
24.9%
U404909
24.7%
R403791
24.6%
P403415
24.6%
A4123
 
0.3%
N3251
 
0.2%
L2577
 
0.2%
O2319
 
0.1%
V2046
 
0.1%
Z1992
 
0.1%
Other values (19)5036
 
0.3%
None
ValueCountFrequency (%)
Ñ64
100.0%

FEC_REGISTRO_ANIO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2019
184270 
2018
83017 
2017
78792 
2016
60350 
2014
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1625724
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2019184270
45.3%
201883017
20.4%
201778792
19.4%
201660350
 
14.8%
20142
 
< 0.1%

Length

2022-08-07T18:49:54.904129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:49:55.009130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2019184270
45.3%
201883017
20.4%
201778792
19.4%
201660350
 
14.8%
20142
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1625724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1625724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1625724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

FEC_REGISTRO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.10687915
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:55.103099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.540079265
Coefficient of variation (CV)0.5796871327
Kurtosis-1.239482606
Mean6.10687915
Median Absolute Deviation (MAD)3
Skewness0.2049469039
Sum2482025
Variance12.5321612
MonotonicityNot monotonic
2022-08-07T18:49:55.187102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
341468
10.2%
140136
9.9%
539766
9.8%
239523
9.7%
439074
9.6%
1133932
8.3%
1233689
8.3%
1031268
7.7%
630505
7.5%
727880
6.9%
Other values (2)49190
12.1%
ValueCountFrequency (%)
140136
9.9%
239523
9.7%
341468
10.2%
439074
9.6%
539766
9.8%
630505
7.5%
727880
6.9%
827787
6.8%
921403
5.3%
1031268
7.7%
ValueCountFrequency (%)
1233689
8.3%
1133932
8.3%
1031268
7.7%
921403
5.3%
827787
6.8%
727880
6.9%
630505
7.5%
539766
9.8%
439074
9.6%
341468
10.2%

FEC_REGISTRO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.73254255
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:55.288129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.755590138
Coefficient of variation (CV)0.5565273453
Kurtosis-1.181069495
Mean15.73254255
Median Absolute Deviation (MAD)8
Skewness0.007484594357
Sum6394193
Variance76.66035867
MonotonicityNot monotonic
2022-08-07T18:49:55.387102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1514020
 
3.4%
1813939
 
3.4%
413838
 
3.4%
1713748
 
3.4%
1613650
 
3.4%
2013598
 
3.3%
1913550
 
3.3%
1113550
 
3.3%
2613508
 
3.3%
2213505
 
3.3%
Other values (21)269525
66.3%
ValueCountFrequency (%)
112621
3.1%
213353
3.3%
313000
3.2%
413838
3.4%
513502
3.3%
613285
3.3%
713249
3.3%
813411
3.3%
913218
3.3%
1012778
3.1%
ValueCountFrequency (%)
318066
2.0%
3011678
2.9%
2912102
3.0%
2813496
3.3%
2713210
3.3%
2613508
3.3%
2513082
3.2%
2412910
3.2%
2313026
3.2%
2213505
3.3%

FEC_REGISTRO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.778978474
Minimum0
Maximum6
Zeros70685
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:55.481099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.011661519
Coefficient of variation (CV)0.7238852473
Kurtosis-1.229345681
Mean2.778978474
Median Absolute Deviation (MAD)2
Skewness0.1445645785
Sum1129463
Variance4.046782069
MonotonicityNot monotonic
2022-08-07T18:49:55.557101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
070685
17.4%
162428
15.4%
260698
14.9%
357709
14.2%
454292
13.4%
652249
12.9%
548370
11.9%
ValueCountFrequency (%)
070685
17.4%
162428
15.4%
260698
14.9%
357709
14.2%
454292
13.4%
548370
11.9%
652249
12.9%
ValueCountFrequency (%)
652249
12.9%
548370
11.9%
454292
13.4%
357709
14.2%
260698
14.9%
162428
15.4%
070685
17.4%

FECHA_HORA_HECHO_ANIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.952371
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:55.649101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile2016
Q12017
median2018
Q32019
95-th percentile2019
Maximum2019
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.129988597
Coefficient of variation (CV)0.0005599679227
Kurtosis2.191699247
Mean2017.952371
Median Absolute Deviation (MAD)1
Skewness-0.7546777071
Sum820158400
Variance1.27687423
MonotonicityNot monotonic
2022-08-07T18:49:55.737128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2019183883
45.2%
201882180
20.2%
201778611
19.3%
201661079
 
15.0%
2015584
 
0.1%
201433
 
< 0.1%
201312
 
< 0.1%
201211
 
< 0.1%
20118
 
< 0.1%
20077
 
< 0.1%
Other values (11)23
 
< 0.1%
ValueCountFrequency (%)
19902
 
< 0.1%
19971
 
< 0.1%
20001
 
< 0.1%
20011
 
< 0.1%
20022
 
< 0.1%
20031
 
< 0.1%
20044
< 0.1%
20065
< 0.1%
20077
< 0.1%
20082
 
< 0.1%
ValueCountFrequency (%)
2019183883
45.2%
201882180
20.2%
201778611
19.3%
201661079
 
15.0%
2015584
 
0.1%
201433
 
< 0.1%
201312
 
< 0.1%
201211
 
< 0.1%
20118
 
< 0.1%
20102
 
< 0.1%

FECHA_HORA_HECHO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.061358509
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:55.828129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.545933345
Coefficient of variation (CV)0.5850063711
Kurtosis-1.241209798
Mean6.061358509
Median Absolute Deviation (MAD)3
Skewness0.2106225906
Sum2463524
Variance12.57364329
MonotonicityNot monotonic
2022-08-07T18:49:55.910098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
142413
10.4%
342064
10.3%
239471
9.7%
438787
9.5%
538446
9.5%
1134018
8.4%
1232339
8.0%
1031064
7.6%
630625
7.5%
727878
6.9%
Other values (2)49326
12.1%
ValueCountFrequency (%)
142413
10.4%
239471
9.7%
342064
10.3%
438787
9.5%
538446
9.5%
630625
7.5%
727878
6.9%
827137
6.7%
922189
5.5%
1031064
7.6%
ValueCountFrequency (%)
1232339
8.0%
1134018
8.4%
1031064
7.6%
922189
5.5%
827137
6.7%
727878
6.9%
630625
7.5%
538446
9.5%
438787
9.5%
342064
10.3%

FECHA_HORA_HECHO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.53156624
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:56.006130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.785783002
Coefficient of variation (CV)0.5656726993
Kurtosis-1.183793609
Mean15.53156624
Median Absolute Deviation (MAD)8
Skewness0.02010296669
Sum6312510
Variance77.18998295
MonotonicityNot monotonic
2022-08-07T18:49:56.103129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
115192
 
3.7%
1514179
 
3.5%
313926
 
3.4%
1713918
 
3.4%
2513664
 
3.4%
413647
 
3.4%
1813607
 
3.3%
1413599
 
3.3%
1013592
 
3.3%
2013534
 
3.3%
Other values (21)267573
65.8%
ValueCountFrequency (%)
115192
3.7%
213390
3.3%
313926
3.4%
413647
3.4%
513283
3.3%
613230
3.3%
713297
3.3%
813126
3.2%
913017
3.2%
1013592
3.3%
ValueCountFrequency (%)
317084
1.7%
3011571
2.8%
2911668
2.9%
2813090
3.2%
2713114
3.2%
2613199
3.2%
2513664
3.4%
2413114
3.2%
2312619
3.1%
2212770
3.1%

FECHA_HORA_HECHO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.123583093
Minimum0
Maximum6
Zeros62021
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:49:56.196129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.109505325
Coefficient of variation (CV)0.6753479137
Kurtosis-1.355535663
Mean3.123583093
Median Absolute Deviation (MAD)2
Skewness-0.05312313012
Sum1269521
Variance4.450012717
MonotonicityNot monotonic
2022-08-07T18:49:56.271129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
679158
19.5%
062021
15.3%
555642
13.7%
154456
13.4%
253689
13.2%
351331
12.6%
450134
12.3%
ValueCountFrequency (%)
062021
15.3%
154456
13.4%
253689
13.2%
351331
12.6%
450134
12.3%
555642
13.7%
679158
19.5%
ValueCountFrequency (%)
679158
19.5%
555642
13.7%
450134
12.3%
351331
12.6%
253689
13.2%
154456
13.4%
062021
15.3%

Interactions

2022-08-07T18:49:45.011975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:33.667727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:35.309726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:36.872726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:38.485430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:40.066098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:41.785096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:43.361098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:45.211003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:33.880733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:35.494762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:37.070726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:38.680095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:40.259099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:41.976129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:43.563096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:45.411012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:34.083726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:35.687757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:37.269725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:38.879096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:40.452095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:42.170094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:43.768790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:45.614984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:34.288761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:35.883725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:37.473726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:39.075131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:40.651095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:42.367101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:43.975785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:45.821009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:34.492730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:36.079726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:37.675460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:39.273132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:40.846097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:42.563128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:44.182977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:46.023010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:34.700758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:36.276763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:37.878431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:39.471097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:41.048098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:42.757129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:44.390981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:46.225003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:34.905729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:36.470762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:38.079438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:39.670099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:41.247105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:42.951095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:44.593976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:46.429976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:35.113755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:36.668726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:38.283430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:39.869104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:41.446138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:43.152097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:49:44.800981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-08-07T18:49:56.359099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-07T18:49:56.509102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-07T18:49:56.658099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-07T18:49:57.022098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-07T18:49:57.179101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-07T18:49:47.406974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-07T18:49:49.424005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
0HUANCHACOJUZGADO DE FAMILIAAV. LA RIVERA PLAZA SAN MARTINHUANCHACOHUANCHACOLA LIBERTADLA LIBERTAD32CONVIVIENTE[DEINPOL] ACTA DE DENUNCIA VERBALMALTRATO SIN LESIONTRUJILLOTRUJILLOREGPOL - LA LIBERTADMLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALSECTOR SANTA ROSA MZ. 11 LT. 36 - HUANCHAQUITO ALTOOtrosPERU20161142016114
1DULANTOJUZGADO DE FAMILIACALLAOCALLAOCALLAOCALLAOCALLAO40SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARMALTRATO SIN LESIONCALLAOCALLAOREGPOL - CALLAOMLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARDENUNCIAMZ. 2 LOTE 31OtrosPERU20161252016114
2JICAMARCAOTROSMz. Q S/N Ovalo Principal de JicamarcaLURIGANCHO - CHOSICALURIGANCHO - CHOSICALIMALIMA28CONVIVIENTE[FAM] DENUNCIA VIOLENCIA FAMILIARMALTRATO SIN LESIONLIMALIMAREGPOL - LIMAFLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARDENUNCIASANTA CRUZOtrosPERU20161362016125
3OCOÑAOTROSAV. PANAMERICA SUR SNOCOÑAOCOÑAAREQUIPAAREQUIPA55SOLTERO(A)[FAM] DENUNCIA ABANDONO Y RETIRO DE HOGARVIOLENCIA PSICOLOGICACAMANACAMANAREGPOL - AREQUIPAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAAA. HH. Gilberto CARNERO Anexo de PlanchadaCarreteraPERU2016136201512265
4PARCONAJUZGADO DE FAMILIAJJ ELIAS S/NPARCONAPARCONAICAICA21SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA FISICAICAICAREGPOL - ICAFLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALEL PURGATORIO MZ- C LT 8 AV 7CallePERU201614020164110
5PUCUSANAJUZGADO DE FAMILIAAv. lima 500PUCUSANAPUCUSANALIMALIMA30SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICA Y PSICOLOGICALIMALIMAREGPOL - LIMAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAManaos Nro.137JironPERU20161402016140
6LA ESPERANZA - JERUSALENJUZGADO DE FAMILIAMz 34 Lte 1 sector wichanzao - Panamericana NorteLA ESPERANZALA ESPERANZALA LIBERTADLA LIBERTAD25SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICA Y PSICOLOGICATRUJILLOTRUJILLOREGPOL - LA LIBERTADMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAMZ. B.38 LOTE 05 - II ETAPA MANUEL AREVALO - LA ESPERANZAOtrosPERU20161402016140
7LA LIBERTADJUZGADO DE FAMILIAJr Leoncio Prado S/N macateCHIMBOTECHIMBOTEANCASHANCASH46CASADO(A)[FAM] OCURRENCIA VIOLENCIA FAMILIARVIOLENCIA FISICASANTASANTAREGPOL - HUARAZMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIAROCURRENCIAJR. CALLAO 132 FLORIDA BAJA - CHIMBOTEAvenidaPERU2016140201512313
8URB. PACHACAMACJUZGADO DE FAMILIAJR. CASTILLA 521 DIST. PACHACAMACVILLA EL SALVADORVILLA EL SALVADORLIMALIMA35CASADO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICALIMALIMAREGPOL - LIMAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAMZ: E-1-LOTE:3- BARRIO:4- 2DO SECTOROtrosPERU20161402016136
9MARANGAJUZGADO DE FAMILIAAV PRECURSORESSAN MIGUELSAN MIGUELLIMALIMA34SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICALIMALIMAREGPOL - LIMAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIALOS SUSPIROSCallePERU20161402016114

Last rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
406421TACNA - GONZALES VIGILOTROSAV INDUSTRIAL S/NTACNATACNATACNATACNA31SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALLEONCIO PRADOOtrosPERU201912311201912311
406422TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA52SOLTERO(A)[FAM] ACTA DE INTERVENCIONVIOLENCIA FISICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE INTERVENCIONCALLE MANCO CAPAC MZ. 10 LOTE 14OtrosPERU201912230201912226
406423TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA41CASADO(A)[FAM] ACTA DE INTERVENCIONVIOLENCIA FISICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE INTERVENCIONURBANIZACION SEÑOR DE LUREN MZ.A LTE.02 CP LA NATIVIDAD - TACNAOtrosPERU201912300201912296
406424TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA50SOLTERO(A)[FAM] ACTA DE INTERVENCIONVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE INTERVENCIONcalle carolina freyre 2273-A NATIVIDADOtrosPERU201912160201912156
406425TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA36SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCALLE NATIVIDAD NRO.2127 CP LA NATIVIDAD - TACNAOtrosPERU201912182201912171
406426TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA56SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCALLE CRISTINA VILDOSO NRO, 1799 - CP. NATIVIDADOtrosPERU20191222620197312
406427TACNA - NATIVIDADOTROScalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA59SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALAlfonso Ugarte II Etapa Mz. C ? 01 Lote 03 ? Gregorio Albarracín TacnaOtrosPERU201912263201912252
406428TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA30SOLTERO(A)[FAM] ACTA DE INTERVENCIONVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE INTERVENCIONCALLE JUAN PABLO VIZCARDO Y GUZMAN NRO.1673 CP LA NATIVIDADOtrosPERU201912300201912263
406429TACNA - NATIVIDADOTROScalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA22SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCalle Moquegua Nro. 242OtrosPERU201912311201912311
406430TARATAJUZGADO DE FAMILIACALLE PRIMERO DE SETIEMBRE S/N TARATATARATATARATATACNATACNA38SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA FISICATARATATARATAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCalle Bolognesi S/N - TarataOtrosPERU20191290201911250

Duplicate rows

Most frequently occurring

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM# duplicates
381UCHUMAYO – CONGATAOTROSURB. EL CARMEN B-2Y3UCHUMAYOUCHUMAYOAREQUIPAAREQUIPA40SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICAAREQUIPAAREQUIPAREGPOL - AREQUIPAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAJUAN EL BUENO II MZ.B LT17 CONGATAOtrosPERU20183190201831754
53CASIMIRO CUADROJUZGADO DE FAMILIACASIMIRO CUADROSCAYMACAYMAAREQUIPAAREQUIPA28SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIAR - RESERVADAVIOLENCIA FISICA Y PSICOLOGICAAREQUIPAAREQUIPAREGPOL - AREQUIPAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAVIRGEN DE LA CANDELARIA MZ. A LOTE. 2 BUENOS AIRES CAYMAOtrosPERU20167235201672243
92COLQUE APAZAJUZGADO DE FAMILIAav jorge chavez s/n paucarpataPAUCARPATAPAUCARPATAAREQUIPAAREQUIPA43CONVIVIENTE[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICA Y PSICOLOGICAAREQUIPAAREQUIPAREGPOL - AREQUIPAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESDENUNCIAAV. LAS MALVINAS C-13, URB. EL PEDREGALOtrosPERU2019613320196743
97COMISARIA DE LA FAMILIAJUZGADO DE FAMILIAAV. BALTA 080CHICLAYOCHICLAYOLAMBAYEQUELAMBAYEQUE21SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICACHICLAYOCHICLAYOREGPOL - LAMBAYEQUEMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAP. JOVEN 4 DE NOVIEMBRE MZ. B LT. 01CallePERU20191302201912913
104COMISARIA DE LA FAMILIAJUZGADO DE FAMILIAHIPOLITO UNANUE N°970TACNATACNATACNATACNA61CASADO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALROSPIGLIOSI N°652 - CercadoCallePERU20168312201682643
148DE LA MUJERJUZGADO DE FAMILIAJR. GRAU S/N - PLAZA RAMON CASTILLAHUANCAVELICAHUANCAVELICAHUANCAVELICAHUANCAVELICA31SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICAHUANCAVELICAHUANCAVELICAREGPOL - HUANCAVELICAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAAV. ERNESTO MORALES N° 188 - ASCENCION - HVCA.AvenidaPERU201688020168653
154EL MANZANOJUZGADO DE FAMILIAJR. SALVADORRIMACRIMACLIMALIMA34SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICALIMALIMAREGPOL - LIMAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAMARIANO MELGAR N° 244 MZ. C LT. 15 ? RIMACOtrosPERU2018411220184863
197JAUJAJUZGADO DE FAMILIAjr. lima s/nJAUJAJAUJAJUNINJUNIN22SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICA Y PSICOLOGICAJAUJAJAUJAREGPOL - HUANCAYOMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAJR. ATAHUALPA N°292 - JAUJAJironPERU2016112322016112323
298PUENTE PIEDRAOTROSAV. LAS ACACIAS JACARANDA PUENTE PIEDRAPUENTE PIEDRAPUENTE PIEDRALIMALIMA27CASADO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICALIMALIMAREGPOL - LIMAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIACOLEGIO 3071 GARCIA SERRONOtrosPERU20183212201832123
308SAN CAYETANOJUZGADO DE FAMILIAJr. Urb. San Cayetano, Sucre 190EL AGUSTINOEL AGUSTINOLIMALIMA47CONVIVIENTE[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICALIMALIMAREGPOL - LIMAMLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALAV. RIVA AGUERO CUADRA 01 - EL AGUSTINOOtrosPERU20191243201912433